electricsheepafrica/africa-climada-earthquake-dataset

Earthquakes: Hazard Data for Disaster Risk Assessment (selected countries)

Publisher: ETH Zürich - Weather and Climate Risks · Source: HDX · License: cc-by · Updated: 2025-04-15

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Abstract

Earthquake hazard sets at 150 arcsec (ca. 4km) resolution, available for the entire globe and per country. Available as historic records from the USGS epicentres database and as a simple probabilistic sampling starting from the historic earthquake catalog, with 9 synthetic events per historic record. The presented data are maximum intensity on the Modified Mercalli intensity scale (MMI) over the historic record 1904-2017.

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-04-15. Geographic scope: AFG, BGD, BFA, BDI, CMR, CAF, TCD, COL, and 19 others.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	First-level administrative unit observations
Rows (total)	330
Columns	9 (3 numeric, 6 categorical, 0 datetime)
Train split	264 rows
Test split	66 rows
Geographic scope	AFG, BGD, BFA, BDI, CMR, CAF, TCD, COL, and 19 others
Publisher	ETH Zürich - Weather and Climate Risks
HDX last updated	2025-04-15

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Variables

Geographic — country_name (Afghanistan, Colombia, DR Congo), admin1_name (North, Bolívar, Amazonas), latitude (range -25.9531–51.3497), longitude (range -81.5417–98.758).

Outcome / Measurement — value (range 0.0–10.5).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-04).

Other — aggregation (max), indicator (earthquake, #indicator+name).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-climada-earthquake-dataset")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
country_name	object	0.0%	Afghanistan, Colombia, DR Congo
admin1_name	object	0.0%	North, Bolívar, Amazonas
latitude	float64	0.3%	-25.9531 – 51.3497 (mean 14.2994)
longitude	float64	0.3%	-81.5417 – 98.758 (mean 16.2111)
aggregation	object	0.3%	max
indicator	object	0.0%	earthquake, #indicator+name
value	float64	0.3%	0.0 – 10.5 (mean 5.5933)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-04

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Numeric Summary

Column	Min	Max	Mean	Median
latitude	-25.9531	51.3497	14.2994	10.2651
longitude	-81.5417	98.758	16.2111	29.9007
value	0.0	10.5	5.5933	6.59

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Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. 3 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from ETH Zürich - Weather and Climate Risks and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• This dataset spans 27 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_climada_earthquake_dataset,
  title     = {Earthquakes: Hazard Data for Disaster Risk Assessment (selected countries)},
  author    = {ETH Zürich - Weather and Climate Risks},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/climada-earthquake-dataset},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.